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Null and Alternative Hypotheses

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...

Statistical Hypothesis Testing

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...

Types of Hypothesis Testing

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...

Decision Making: Traditional Method

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...

One-Way ANOVA: Unequal Sample Sizes

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

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Related Experiment Video

Updated: Oct 1, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Testing a global null hypothesis using ensemble machine learning methods.

Sunwoo Han¹, Youyi Fong¹, Ying Huang¹

¹Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.

Statistics in Medicine

|March 7, 2022

Summary

This summary is machine-generated.

This study enhances biomarker discovery by using ensemble machine learning to improve statistical testing power for identifying significant predictors of binary outcomes in biomedical research.

Keywords:

AUC cross validation hypothesis test random forest stacking vaccine efficacy trial

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Biostatistics
Machine Learning
Biomedical Informatics

Background:

Identifying significant predictors for binary outcomes from large biomarker datasets is crucial in biomedical research.
Existing statistical methods may lack the power to detect subtle but important relationships.

Purpose of the Study:

To enhance the statistical power of hypothesis testing for identifying significant predictors of binary outcomes.
To leverage ensemble machine learning methods for improved biomarker discovery.

Main Methods:

Utilized ensemble machine learning techniques including random forest, bagging, adaptive boosting, and stacking.
Applied nonparametric modeling to capture complex relationships between biomarkers and binary outcomes.
Validated the proposed methods through Monte Carlo simulations.

Main Results:

Demonstrated increased power in hypothesis testing for biomarker significance compared to traditional methods.
Successfully applied the ensemble methods to a real-world immunologic biomarker dataset from the RV144 HIV vaccine trial.

Conclusions:

Ensemble machine learning methods offer a powerful approach for identifying significant biomarkers in complex biomedical datasets.
These methods can improve the discovery of predictors for binary outcomes, advancing biomedical research and clinical applications.